What exactly is the difference between deep learning and machine learning?

If you’re interested in the world of artificial intelligence, there’s a good chance that you’ll have already come across the terms deep learning and machine learning. Although these two concepts sound relatively similar, they’re not quite the same thing – something we’ll unpack in this blog post.

First things first: machine learning is officially defined as “the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data”. Essentially, it’s a branch of artificial intelligence that uses data and algorithms to mimic the learning processes of the human brain. It’s an important and growing field within the realm of data science, and can be used to derive actionable insights from raw data.

Deep learning, on the other hand, is actually a sub-genre of machine learning (and, by extension, artificial intelligence). It adopts the same stance as machine learning in terms of its approach to mimicking the human brain, but leans more into a concept known as artificial neural networking.

To get technical, artificial neural networks are computing systems that loosely model themselves on the synapses in our brains. Each artificial neuron in this system is able to process transmit a signal – which are essentially a series of numbers - to other artificial neurons using these synapses.

Deep learning takes a deep dive into this, and aims to construct these large neural networks with the intention of making learning algorithms more efficient and easier to use, as well as making revolutionary advancements in machine learning and artificial intelligence in general. For example, as we gradually build bigger and better neural networks and train them using increasingly larger amounts of data, their performance will continue to grow. This is a little different to traditional machine learning, which plateaus at a certain point.

To be honest, the full potential of deep learning technologies hasn’t yet been fully realised – but it looks very promising when it comes to finding solutions to some of medicine’s most stubborn problems. This is mainly due to the scalability of the artificial neural network model, which opens up a world of possibility across a wide variety of genres within the healthcare sector.

Both machine learning and deep learning are fields which are continually evolving, and we’re excited to see what the future of this branch of artificial intelligence looks like. In particular, deep learning is hugely scalable across domains, and will allow us to explore increasingly complex concepts in greater detail than ever before.

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